Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data.
Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data.
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DOI：
10.1158/1535-7163.mct-07-0177
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发表时间：
2008-05
影响因子：
5.7
通讯作者：
Collins VP
中科院分区：
文献类型：
--
作者：
Petalidis LP;Oulas A;Backlund M;Wayland MT;Liu L;Plant K;Happerfield L;Freeman TC;Poirazi P;Collins VP
根据当前WHO标准对星形细胞肿瘤进行组织学分级，提供了一种有价值但简化的肿瘤学现实表现，通常不足以预测临床结果。在这项研究中，我们报告了一个新的星形细胞肿瘤基因表达芯片数据集（n=65）。我们已经使用了一个简单的人工神经网络（ANN）算法来解决人类星形细胞肿瘤的分级，从星形细胞肿瘤的组织病理学亚型中获得特定的转录特征，并评估这些分子特征是否定义了生存预后亚类。鉴定了59个分类器基因，发现它们属于三个不同的功能类别，即血管生成、细胞分化和低级别星形细胞肿瘤辨别。发现这些基因类别表征三种分子肿瘤亚型，表示为ANGIO、INTER和LOWER。使用这些亚型的样本分级与我们的数据集（96.15%）以及独立数据集的先前组织病理学分级一致。六种肿瘤在组织病理学诊断方面特别具有挑战性。我们提出了一个人工神经网络分级这些样本，并提供了一个基于证据的解释分级结果，使用临床元数据，以证实调查结果。发现三种确定的肿瘤亚型的预后价值优于组织病理学分级以及其他研究中报告的肿瘤亚型，表明59个基因分类器具有较高的生存预后潜力。最后，还确定了11个区分原发性和继发性胶质母细胞瘤的基因分类器。
Histopathological grading of astrocytic tumours based on current WHO criteria offers a valuable but simplified representation of oncological reality and is often insufficient to predict clinical outcome. In this study we report a new astrocytic tumour microarray gene expression dataset (n=65). We have used a simple Artificial Neural Network (ANN) algorithm to address grading of human astrocytic tumours, derive specific transcriptional signatures from histopathological subtypes of astrocytic tumours and asses whether these molecular signatures define survival prognostic subclasses. 59 classifier genes were identified and found to fall within three distinct functional classes namely angiogenesis, cell differentiation and lower grade astrocytic tumour discrimination. These gene classes were found to characterize three molecular tumour subtypes denoted ANGIO, INTER and LOWER. Grading of samples using these subtypes agreed with prior histopathological grading both for our dataset (96.15%) as well as an independent dataset. Six tumours were particularly challenging to diagnose histopathologically. We present an ANN grading for these samples, and offer an evidence-based interpretation of grading results using clinical metadata to substantiate findings. The prognostic value of the three identified tumour subtypes was found to outperform histopathological grading as well as tumour subtypes reported in other studies, indicating a high survival prognostic potential for the 59 gene classifiers. Finally, 11 gene classifiers that differentiate between primary and secondary glioblastomas were also identified.